SLAM (Simultaneous Localization and Mapping) has become a cornerstone technology in robotics, drones, autonomous driving, and AR/VR. By leveraging sensors, SLAM enables autonomous localization, mapping, and path planning. Depending on the sensor type, SLAM is typically classified into two categories: Laser SLAM and Visual SLAM.
Laser SLAM appeared earlier and has reached greater maturity in theory, technology, and commercial deployment. In contrast, Visual SLAM is still in rapid development. Its two primary approaches are:
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RGB-D cameras (e.g., Kinect) that directly provide depth information;
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Monocular, stereo, or fisheye cameras that estimate depth and motion through image sequences.
While Visual SLAM is gaining traction, especially with the progress of computer vision, it remains in the process of expanding applications and stabilizing productization.
Laser SLAM
By 2005, the basic framework of Laser SLAM was already well established. It remains the most stable and widely adopted localization and navigation method today.
Laser SLAM Mapping
Visual SLAM
With the boom in computer vision, Visual SLAM has drawn attention for its rich information content and wide applicability.
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RGB-D-based approaches generate point clouds similar to LiDAR, allowing direct distance measurement.
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Monocular/stereo/fisheye-based approaches estimate pose changes from successive frames and accumulate them to infer distances and build maps.
Visual SLAM Mapping
In both industry and academia, there has been ongoing debate regarding the relative merits of Laser SLAM and Visual SLAM, as well as which approach will emerge as the dominant paradigm in the future. The following provides a comparative analysis of the two methods across several key dimensions.
Cost
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Laser SLAM: High cost (e.g., SICK, Hokuyo, Velodyne ranging from tens of thousands to hundreds of thousands of RMB). However, affordable domestic solutions such as SLAMTEC RPLIDAR are available.
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Visual SLAM: Cameras are much cheaper, but accuracy in obstacle detection and distance measurement is generally lower compared to LiDAR.
Application Scenarios
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Laser SLAM: Primarily used indoors for mapping and navigation.
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Visual SLAM: Works in both indoor and outdoor settings but is highly dependent on lighting and cannot function well in darkness or low-texture areas.
Mapping Accuracy
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Laser SLAM: High accuracy; SLAMTEC’s RPLIDAR achieves ~2 cm map accuracy.
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Visual SLAM: Kinect depth cameras (3–12 m range) achieve ~3 cm accuracy. In general, Laser SLAM is more precise and directly usable for navigation.
Ease of Use
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Laser SLAM and RGB-D Visual SLAM: Directly obtain point cloud data for obstacle detection.
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Monocular/stereo Visual SLAM: Cannot directly get point clouds; instead, relies on feature extraction/matching and triangulation, making it more complex.
Installation
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Early LiDARs (e.g., Velodyne) were large and heavy, unsuitable for drones, AR, or VR.
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Visual SLAM with cameras offers smaller form factor, flexible installation, and better aesthetics/performance for compact devices.
Other Factors
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Differences exist in detection range, computational intensity, real-time data generation, and accumulated map errors.
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For example, KITTI benchmark data shows that Visual SLAM often exhibits drift errors over time, requiring loop closure detection, whereas Laser SLAM remains more stable.
Note: Left Lidar SLAM, Right Visual SLAM
It is evident that in the same environment, Visual SLAM tends to deviate in the later stages due to accumulated error. This makes loop closure detection essential for correcting drift.
Conclusion
Laser SLAM remains the most mature and stable solution for autonomous localization and navigation, while Visual SLAM represents an important direction for future research and applications. Looking ahead, multi-sensor fusion is expected to become the mainstream trend, combining the precision of LiDAR with the rich information of vision.
SLAMTEC is committed to continually refining and upgrading its Laser SLAM solutions, while integrating complementary technologies to deliver practical, reliable, and user-friendly localization and navigation systems.
Comparison of Laser SLAM and Visual SLAM: Advantages and Disadvantages
Comparison of Laser SLAM and Visual SLAM: Advantages and Disadvantages
